NumPy was created in 2005 by Travis Oliphant by combining features of Numarray into Numeric package.
Its main objects are multi-Dimensional (D) array. It is a table of elements, indexed by a tuple of positive integers.
Think of these arrays as tables filled with elements, and you can access those elements using a combination of positive integers.
To help you understand this concept better, let's look at an example:
[[1, 0, 8],
[0, 1, 1]]
In this example:
- The first dimension (or axis) has a length of 2
- The second dimension (or axis) has a length of 3
- The rank of this array is 2, which means it's a 2-dimensional (2-D) array.
Axes : NumPy Dimensions
Rank : No. of axes
NumPy array class is known as ndarray.
Attributes of ndarray class:
Ndarray has several attributes that are useful to know:
- ndarray.ndim : This tells Rank of array which is number of axes of the array.
- ndarray.shape : This provides the dimensions of array. This is a tuple of integers indicating the size of array.
(n, m) : n(rows), m(column).
- ndarray.size : Total number of elements in the array (
n * m).
- ndarray.dtype : An object describing the type of elements in the array. One can create or specify data types using standard Python types. e.g.
numpy.int32, numpy.float 64
- ndarray.itemsize :
dtype/8 – Equivalent to ndarray.dtype.itemsize.
- ndarray.data : Actual elements of the array are stored in this buffer.
Python program for illustration:
Let's see a Python code example to illustrate the working of the ndarray class:
# Python Program illustrating the working of ndarray class
import numpy as np
# Array declaration
a = np.arange(10).reshape(2, 5)
print("array : \n", a)
# use of ndarray.shape
print("\nndarray.shape : ", a.shape, "\n")
# use of ndarray.ndim
print("ndarray.ndim : ", a.ndim, "\n")
# use of ndarray.dtype
print("ndarray.dtype : ", a.dtype, "\n")
# use of ndarray.size
print("ndarray.size : ", a.size, "\n")
# use of ndarray.itemsize - dtype/8
print("ndarray.itemsize : ", a.itemsize, "\n")
Output :
array :
[[0 1 2 3 4]
[5 6 7 8 9]]
ndarray.shape : (2, 5)
ndarray.ndim : 2
ndarray.dtype : int32
ndarray.size : 10
ndarray.itemsize : 4
I hope these additional explanations help you understand the basics of NumPy's ndarray class.
